Continuity and differentiability of regression M functionals
This paper deals with the Fisher-consistency, weak continuity and differentiability of estimating functionals corresponding to a class of both linear and nonlinear regression high breakdown M estimates, which includes S and MM estimates. A restricted type of differentiability, called weak differenti...
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Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_13507265_v18_n4_p1284_Fasano |
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todo:paper_13507265_v18_n4_p1284_Fasano2023-10-03T16:10:03Z Continuity and differentiability of regression M functionals Fasano, M.V. Maronna, R.A. Sued, M. Yohai, V.J. Asymptotic normality Consistency MM estimates Nonlinear regression S estimates This paper deals with the Fisher-consistency, weak continuity and differentiability of estimating functionals corresponding to a class of both linear and nonlinear regression high breakdown M estimates, which includes S and MM estimates. A restricted type of differentiability, called weak differentiability, is defined, which suffices to prove the asymptotic normality of estimates based on the functionals. This approach allows to prove the consistency, asymptotic normality and qualitative robustness of M estimates under more general conditions than those required in standard approaches. In particular, we prove that regression MMestimates are asymptotically normal when the observations are φ-mixing. © 2012 ISI/BS. Fil:Maronna, R.A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Sued, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_13507265_v18_n4_p1284_Fasano |
institution |
Universidad de Buenos Aires |
institution_str |
I-28 |
repository_str |
R-134 |
collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
topic |
Asymptotic normality Consistency MM estimates Nonlinear regression S estimates |
spellingShingle |
Asymptotic normality Consistency MM estimates Nonlinear regression S estimates Fasano, M.V. Maronna, R.A. Sued, M. Yohai, V.J. Continuity and differentiability of regression M functionals |
topic_facet |
Asymptotic normality Consistency MM estimates Nonlinear regression S estimates |
description |
This paper deals with the Fisher-consistency, weak continuity and differentiability of estimating functionals corresponding to a class of both linear and nonlinear regression high breakdown M estimates, which includes S and MM estimates. A restricted type of differentiability, called weak differentiability, is defined, which suffices to prove the asymptotic normality of estimates based on the functionals. This approach allows to prove the consistency, asymptotic normality and qualitative robustness of M estimates under more general conditions than those required in standard approaches. In particular, we prove that regression MMestimates are asymptotically normal when the observations are φ-mixing. © 2012 ISI/BS. |
format |
JOUR |
author |
Fasano, M.V. Maronna, R.A. Sued, M. Yohai, V.J. |
author_facet |
Fasano, M.V. Maronna, R.A. Sued, M. Yohai, V.J. |
author_sort |
Fasano, M.V. |
title |
Continuity and differentiability of regression M functionals |
title_short |
Continuity and differentiability of regression M functionals |
title_full |
Continuity and differentiability of regression M functionals |
title_fullStr |
Continuity and differentiability of regression M functionals |
title_full_unstemmed |
Continuity and differentiability of regression M functionals |
title_sort |
continuity and differentiability of regression m functionals |
url |
http://hdl.handle.net/20.500.12110/paper_13507265_v18_n4_p1284_Fasano |
work_keys_str_mv |
AT fasanomv continuityanddifferentiabilityofregressionmfunctionals AT maronnara continuityanddifferentiabilityofregressionmfunctionals AT suedm continuityanddifferentiabilityofregressionmfunctionals AT yohaivj continuityanddifferentiabilityofregressionmfunctionals |
_version_ |
1807318835574865920 |